October 2025 arXiv papers — page 32

Vignesh Kottayam Viswanathan, Yifan Bai, Scott Fredriksson, Sumeet Satpute

In this work, we present a hierarchical framework designed to support robotic inspection under environment uncertainty. By leveraging a known environment model, existing methods plan and safely track inspection routes to visit points of interest. However, discrepancies between the model and actual site conditions, caused by either natural or human activities

Mingyu Huang, Shasha Zhou, Ke Li

Machine learning models increasingly map biological sequence-fitness landscapes to predict mutational effects. Effective evaluation of these models requires benchmarks curated from empirical data. Despite their impressive scales, existing benchmarks lack topographical information regarding the underlying fitness landscapes, which hampers interpretation and c

Yaniv Nikankin, Dana Arad, Itay Itzhak, Anja Reusch

One of the main challenges in mechanistic interpretability is circuit discovery, determining which parts of a model perform a given task. We build on the Mechanistic Interpretability Benchmark (MIB) and propose three key improvements to circuit discovery. First, we use bootstrapping to identify edges with consistent attribution scores. Second, we introduce a

Prabal Adhikari, Brian Tiburzi, Sona Baghiyan

For a gas confined in a container, particle-wall interactions produce modifications to the partition function involving the average surface density of gas particles. While such correlations have a vanishing effect in the thermodynamic limit, examining them is beneficial for a sharper understanding of how the limit is attained. We contrast a classical and a q

Gang Chen, Changshuo Liu, Gene Anne Ooi, Marcus Tan

Generative Artificial Intelligence (GenAI) is taking the world by storm. It promises transformative opportunities for advancing and disrupting existing practices, including healthcare. From large language models (LLMs) for clinical note synthesis and conversational assistance to multimodal systems that integrate medical imaging, electronic health records, an

Nathan Franel, Vincent Tatischeff, David Murphy, Alexey Ulyanov

COMCUBE-S (COMCUBE-S (Compton Telescope CubeSat Swarm) is a proposed mission aimed at understanding the radiation mechanisms of ultra-relativistic jets from Gamma-Ray Bursts (GRBs). It consists of a swarm of 16U CubeSats carrying a state-of-the-art Compton polarimeter and a bismuth germanium oxide (BGO) spectrometer to perform timing, spectroscopic and polar

Jonathan Gratus, Spyridon Talaganis, Willow Sparks

In this paper, we formulate the distributional uncharged and charged stress-energy tensors. These are integrals, along a worldline, of derivatives of the delta-function. These distributions are also multipoles and they are prescribed to any order. They represent an extended region of non-self-interacting uncharged or charged dust, shrunken to a single point

Nicola Tuccari, Eva Sciacca, Yolanda Becerra, Enric Sosa Cintero

The Astronomy & Cosmology (A&C) community is presently witnessing an unprecedented growth in the quality and quantity of data coming from simulations and observations. Writing results of numerical simulations to disk files has long been a bottleneck in high-performance computing. To access effectively and extract the scientific content of such large-scale da

Lingyi Wang, Rashed Shelim, Walid Saad, Naren Ramakrishnan

Despite the popularity of reinforcement learning (RL) in wireless networks, existing approaches that rely on model-free RL (MFRL) and model-based RL (MBRL) are data inefficient and short-sighted. Such RL-based solutions cannot generalize to novel network states since they capture only statistical patterns rather than the underlying physics and logic from wir

Nicola Tuccari, Eva Sciacca, Yolanda Becerra, Enric Sosa Cintero

Modern simulations and observations in Astronomy & Cosmology (A&C) produce massively large data volumes, posing significant challenges for storage, access and data analysis. A long-standing bottleneck in high-performance computing, especially now in the exascale era, has been the requirement to write these large datasets to disks, which limits the performanc

Weijing Bao, Zhenhao Wang, Jia-Rui Luo, Kui-Tian Xi

We develop a preconditioned nonlinear conjugate-gradient solver for ground states of binary dipolar Bose-Einstein condensates within the extended Gross-Pitaevskii equation including Lee-Huang-Yang corrections. The optimization is carried out on the product-of-spheres normalization manifold and combines a manifold-preserving analytic line search, derived from

Runqing Yang, Pablo Villanueva-Perez, Maik Kahnt

Multi-beam ptychography (MBP) offers a scalable solution to improve the throughput of state-of-the-art ptychography by increasing the number of coherent beams that illuminate the sample simultaneously. However, increasing the number of beams in ptychography makes ptychographical reconstructions more challenging and less robust. It has been demonstrated that

Seyoung Song, Nawon Kim, Songeun Chae, Kiwoong Park

The history of the Korean language is characterized by a discrepancy between its spoken and written forms and a pivotal shift from Chinese characters to the Hangul alphabet. However, this linguistic evolution has remained largely unexplored in NLP due to a lack of accessible historical corpora. To address this gap, we introduce the Open Korean Historical Cor

Linus Kramer, Karl Heinrich Hofmann

We consider several questions related to Pontryagin duality in the category of abelian pro-Lie groups.

Venkata S Govindarajan, Laura Biester

Textual humor is enormously diverse and computational studies need to account for this range, including intentionally bad humor. In this paper, we curate and analyze a novel corpus of sentences from the Bulwer-Lytton Fiction Contest to better understand "bad" humor in English. Standard humor detection models perform poorly on our corpus, and an analysis of l

Qidong He, Ian Jauslin, Joel Lebowitz, Ron Peled

We prove the existence of a phase transition in dimension $d>1$ in a continuum particle system interacting with a pair potential containing a modified attractive Kac potential of range $\gamma^{-1}$, with $\gamma>0$. This transition is "close", for small positive $\gamma$, to the one proved previously by Lebowitz and Penrose in the van der Waals limit $\gamm

Isabella Costa Maia, Marco Congedo, Pedro L. C. Rodrigues, Salem Said

The present work introduces curvature-based rejection sampling (CURS). This is a method for sampling from a general class of probability densities defined on Riemannian manifolds. It can be used to sample from any probability density which ``depends only on distance". The idea is to combine the statistical principle of rejection sampling with the geometric p

David B. Brückner, Gašper Tkačik

Developmental patterning comprises processes that range from purely instructed, where external signals specify cell fates, to fully self-organized, where spatial patterns emerge autonomously through cellular interactions. We propose that both extremes -- as well as the continuum of intermediate cases -- can be conceptualized as information processing systems

Xin Jin, Nitish Kumar Chandra, Mohadeseh Azari, Kaushik P. Seshadreesan

Quantum networks rely on both quantum and classical channels for coordinated operation. Current architectures employ entanglement distribution and key exchange over quantum channels but often assume that classical communication is sufficiently secure. In practice, classical channels protected by traditional cryptography remain vulnerable to quantum adversari

Yuan Shen, Yuze Hong, Guangyang Zeng, Tengfei Zhang

Accurate visual inertial simultaneous localization and mapping (VI SLAM) for underwater robots remains a significant challenge due to frequent visual degeneracy and insufficient inertial measurement unit (IMU) motion excitation. In this paper, we present GeVI-SLAM, a gravity-enhanced stereo VI SLAM system designed to address these issues. By leveraging the s

Francesca Cottini, Maximilian Nitzschner

We consider the model of a directed polymer in a random environment defined on the infinite cluster of supercritical Bernoulli bond percolation in dimensions $d \geq 3$. For this model, it was proved in arXiv:2205.06206 that for almost every realization of the cluster, the polymer is in a strong disorder regime for any positive inverse temperature. Here, we

Simon Schirren

We define a perfect obstruction theory for a moduli of symplectic Higgs sheaves $(E,\phi)$ on projective surfaces $S$. Key to this is a minimality assumption on $\textrm{ch}(E)$ that forces all $E$ to be locally free. This might have implications to define a virtual count and $Sp(r)$-Vafa-Witten invariants.

Eric G. C. Laporte

Many words are ambiguous in terms of their part of speech (POS). However, when a word appears in a text, this ambiguity is generally much reduced. Disambiguating POS involves using context to reduce the number of POS associated with words, and is one of the main challenges of lexical tagging. The problem of labeling words by POS frequently arises in natural

Klaus Kroencke

We review recent results relating linear stability to dynamical stability and the scalar curvature rigidity of Einstein manifolds. We discuss closed and open Einstein manifolds as well as complete noncompact Einstein manifolds which are asymptotically locally Euclidean and asymptotically hyperbolic. For these classes, the relation to the positive mass theore

Zihan Chen, Song Wang, Xingbo Fu, Chengshuai Shi

The capability of in-context learning (ICL) enables large language models (LLMs) to perform novel tasks without parameter updates by conditioning on a few input-output examples. However, collecting high-quality examples for new or challenging tasks can be costly and labor-intensive. In this work, we propose a cost-efficient two-stage pipeline that reduces re

Rishi Das, Harsha Hutridurga, Amiya K. Pani, Ricardo Ruiz-Baier

We derive parameter-robust quasi-optimal error estimates for mixed finite element methods for the nonlinear Darcy--Forchheimer equations with mixed boundary conditions. Using the framework of operator preconditioning, we also design efficient block preconditioners for the linearised system, that exhibit robustness with respect to the coefficients that modula

Lorenzo Ghilotti, Federico Camerlenghi, Tommaso Rigon, Michele Guindani

Feature and trait allocation models are fundamental objects in Bayesian nonparametrics and play a prominent role in several applications. Existing approaches, however, typically assume full exchangeability of the data, which may be restrictive in settings characterized by heterogeneous but related groups. In this paper, we introduce a general and tractable c

Terry Generet, Rene Poncelet, Miha Muškinja

The production of heavy-flavor hadrons in association with a vector boson in proton-proton collisions is a powerful probe for studying Quantum Chromodynamics and the content of protons. In this article, we provide, for the first time, differential predictions through NNLO for the production of $W^{\pm}D^{(*)\mp}$ final states. The results are compared to rec

Bohong Wu, Mengzhao Chen, Xiang Luo, Shen Yan

Large Language Models (LLMs) are powerful but often too slow and costly for real-world use during inference. Looped transformers save on parameters by reusing the same weights for multiple computational steps, or "loops." However, this approach has a major flaw: the loops run one after another, causing inference latency and memory requirements to increase wi

Rafal Gajewski, Ravindra T Desai, James Bateman, Bengt Eliasson

Accurately observing the rarefied media of the upper atmosphere, exosphere, and planetary and solar system environments and beyond requires highly sensitive metrological techniques. We present the operating concept and architecture of an in-situ sensing solution based on the dynamics of a levitated nanoparticle (levitodynamics). It can detect and measure imp

Samuel Del Fré, Andrée de Backer, Christophe Domain, Ludovic Thuinet

Neutron irradiation produces, within a few picoseconds, displacement cascades that are sequences of atomic collisions generating point and extended defects which subsequently affects the long-term evolution of materials. The diversity of these defects, characterized morphologically and statistically, defines what is called the "primary damage". In this work,

Gianluca Stellin, Karl-Heinz Speidel

The relevance of the point-symmetry group $\mathcal{D}_{4h}$ for the prediction of spectrum and electromagnetic properties of the $^{24}\mathrm{Mg}$ nucleus is discussed in the framework of the geometric $\alpha$-cluster model at leading order. The latter represents a macroscopic $\alpha$-cluster framework wherein nuclear excitations are described in terms o

Nicolas Nessi, Peter Reimann

Considering deterministic classical lattice systems with continuous variables, we show that, if the initial conditions are sampled according to a probability distribution in which the dynamical variables are statistically independent, the dynamical trajectory of any macroscopic observable is approximately the same for the vast majority of the states in the s

Tanmoy Mondal, Pinaki Majumdar

We map out the complete transport phase diagram of the ferromagnetic Heisenberg-Kondo lattice model in two dimensions. The model involves tight-binding electrons with hopping $t$, coupled to classical spins with coupling $J'$, while the spins have a nearest neighbour coupling $J$ between them. We work with a fixed, small $J/t$, and study the temperature depe

Rinku Sebastian, Simon O'Keefe, Martin Trefzer

Extracting features from the speech is the most critical process in speech signal processing. Mel Frequency Cepstral Coefficients (MFCC) are the most widely used features in the majority of the speaker and speech recognition applications, as the filtering in this feature is similar to the filtering taking place in the human ear. But the main drawback of this

Susan Ariel Aaronson, Michael Moreno

This study examines whether leading AI chatbot companies implement the responsible AI principles they publicly advocate. The authors used a mixed-methods approach analyzing four major chatbots (ChatGPT, Gemini, DeepSeek, and Grok) across company websites, technical documentation, and direct chatbot evaluations. We found significant gaps between corporate rhe

Romain Poletti, Emanuele Bombardi, Lilla Koloszar, Miguel Alfonso Mendez

The extraordinary aerial agility of hummingbirds and insects continues to inspire the design of flapping-wing drones. To replicate and analyze such flight, computational fluid dynamics (CFD) simulations that couple flow solvers with rigid body dynamics are essential. While OpenFOAM offers tools for these multiphysics simulations, two key limitations remain:

Nicolas Burq, Hui Zhu

We prove that on a compact Riemannian manifold, resolvent bounds for the Laplace--Beltrami operator imply observability, and thus controllability, for the Schr\"odinger propagator from time sets of positive Lebesgue measure. Applications include almost all cases where observability and controllability hold from time intervals, particularly when the geometric

Antonio Condorelli, Jonathan Biteau, Olivier Deligny, Remi Adam

The origin of ultra-high-energy cosmic rays (UHECRs) remains an open questions in astrophysics. We explore two primary scenarios for the distribution of UHECR sources, assuming that their production rate follows either the cosmic star-formation-rate or stellar-mass density. By jointly fitting the UHECR energy spectrum and mass composition measured by the Pie

Marten C. Steketee, Nina M. Verheijen, L. Thomas van Binsbergen

Governmental organisations cope with many laws and policies when handling administrative law cases. Making sure these norms are enforced in the handling of cases is for the most part done manually. However, enforcing policies can get complicated and time consuming with ever-changing (interpretations of) laws and varying cases. This introduces errors and dela

Huanyu Zhang, Wenshan Wu, Chengzu Li, Ning Shang

While Multimodal Large Language Models (MLLMs) excel at visual understanding, they often struggle in complex scenarios that require visual planning and imagination. Inspired by how humans use sketching as a form of visual thinking to develop and communicate ideas, we introduce Latent Sketchpad, a framework that equips MLLMs with an internal visual scratchpad

Jan Paseka, Thomas Vetterlein

An orthoset is a non-empty set $X$ together with a symmetric binary relation $\perp$ and a constant $0$ such that $x \not\perp x$ for any $x \neq 0$, and $0 \perp x$ for any $x$. Maps $f \colon X \to Y$ and $g \colon Y \to X$ between orthosets are said to form an adjoint pair if, for any $x \in X$ and $y \in Y$, $f(x) \perp g$ if and only if $x \perp g(x)$.

Ciaran O'Connor, Mohamed Bahloul, Steven Prestwich, Andrea Visentin

Electricity price forecasting has become a critical tool for decision-making in energy markets, particularly as the increasing penetration of renewable energy introduces greater volatility and uncertainty. Historically, research in this field has been dominated by point forecasting methods, which provide single-value predictions but fail to quantify uncertai

Magnus Heimpel, Irena Hajnsek, Othmar Frey

In multitemporal InSAR, phase linking (PL) refers to the estimation of a single-reference interferometric phase history for distributed scatterers (DS) from the information contained in the sample coherence matrix. Because the phase information in this matrix is typically inconsistent, DS processing needs practical reliability indicators to decide whether a

Inclusion AI, :, Bowen Ma, Cheng Zou

We propose Ming-Flash-Omni, an upgraded version of Ming-Omni, built upon a sparser Mixture-of-Experts (MoE) variant of Ling-Flash-2.0 with 100 billion total parameters, of which only 6.1 billion are active per token. This architecture enables highly efficient scaling (dramatically improving computational efficiency while significantly expanding model capacit

Zheng Zhu, Jingwen Wang, Hao Yu, Jialin Lu

The unique energy band and crystal structure of the layered type-II Weyl semimetal TaIrTe4 hold great promise for high-performance broadband anisotropic optoelectronic devices. Therefore, gaining an in-depth understanding of the interactions between internal microscopic particles is of vital importance. Here, we employ a two-color pump-probe system to reveal

Hugo Alcaraz-Herrera, Michail-Antisthenis Tsompanas, Igor Balaz, Andrew Adamatzky

Soft robotics are increasingly favoured in specific applications such as healthcare, due to their adaptability, which stems from the non-linear properties of their building materials. However, these properties also pose significant challenges in designing the morphologies and controllers of soft robots. The relatively short history of this field has not yet

Carlos Flores-Garrigos, Gaurav Dev, Michael Falkenthal, Alejandro Gomez Cadavid

We design and implement a quantum combinatorial reasoning framework for large language models (QCR-LLM), integrating a real quantum computer in the hybrid workflow. QCR-LLM reformulates reasoning aggregation as a higher-order unconstrained binary optimization (HUBO) problem. In this sense, reasoning fragments are represented as binary variables and their int

Haoying Li, Yifan Peng, Xinghan Li, Junfeng Wu

Reliable state estimation hinges on accurate specification of sensor noise covariances, which weigh heterogeneous measurements. In practice, these covariances are difficult to identify due to environmental variability, front-end preprocessing, and other reasons. We address this by formulating noise covariance estimation as a bilevel optimization that, from a

Tamanna Chatterjee

We develop a theory of primitive pairs for $\mathbb{Z}$-graded Lie algebras when the sheaves have coefficients in a field $\Bbbk$ of positive characteristic, providing a graded analogue of the role played by cuspidal pairs in the generalized Springer correspondence. We consider the centralizer $G_0$ of a fixed cocharacter $\chi$ in a connected, reductive, al

Qing Zong, Jiayu Liu, Tianshi Zheng, Chunyang Li

Accurate confidence calibration in Large Language Models (LLMs) is critical for safe use in high-stakes domains, where clear verbalized confidence enhances user trust. Traditional methods that mimic reference confidence expressions often fail to capture the reasoning needed for accurate confidence assessment. We propose natural language critiques as a soluti

Charlotte Bartnick

Adapting a proof of Bouscaren and Delon, we show that every type-definable connected group in a given stable theory of fields embeds into an algebraic group, under a condition on the definable closure. We also present general hypotheses which yield a uniform description of the definable closure in such theories of fields. The setting includes in particular t

Mortesa Hussaini, Jan Theiß, Anthony Stein

In the context of Federated Learning with heterogeneous data environments, local models tend to converge to their own local model optima during local training steps, deviating from the overall data distributions. Aggregation of these local updates, e.g., with FedAvg, often does not align with the global model optimum (client drift), resulting in an update th

Renato Iturriaga, Ezequiel Maderna

We consider several $N$-body problems. The main result is a very simple and natural criterion for decoupling the Jacobi equation for some classes of them. If $E$ is a Euclidean space, and the potential function $U(x)$ for the $N$-body problem is a $C^2$ function defined in an open subset of $E^N$, then the Jacobi equation along a given motion $x(t)$ writes $

Yong Huang, Zhongqi Yang, Amir Rahmani

Sepsis is a leading cause of mortality in intensive care units (ICUs), yet existing research often relies on outdated datasets, non-reproducible preprocessing pipelines, and limited coverage of clinical interventions. We introduce MIMIC-Sepsis, a curated cohort and benchmark framework derived from the MIMIC-IV database, designed to support reproducible model

Tejaswini Bollikonda

As machine learning (ML) models become increasingly deployed through cloud infrastructures, the confidentiality of user data during inference poses a significant security challenge. Homomorphic Encryption (HE) has emerged as a compelling cryptographic technique that enables computation on encrypted data, allowing predictions to be generated without decryptin

Yuanhang Qian, Kunlong Zhao, Jilu Jin, Xueqin Luo

Fixed beamforming is widely used in practice since it does not depend on the estimation of noise statistics and provides relatively stable performance. However, a single beamformer cannot adapt to varying acoustic conditions, which limits its interference suppression capability. To address this, adaptive convex combination (ACC) algorithms have been introduc

Ruiyang Zhang, Jiahao Luo, Xiaoru Feng, Qiufan Pang

With the rapid advancement of text-to-image (T2I) models, ensuring their safety has become increasingly critical. Existing safety approaches can be categorized into training-time and inference-time methods. While inference-time methods are widely adopted due to their cost-effectiveness, they often suffer from limitations such as over-refusal and imbalance be

Jean-Pierre Florens, Anna Simoni

We develop a structural framework for modeling and inferring unobserved heterogeneity in dynamic panel-data models. Unlike methods treating clustering as a descriptive device, we model heterogeneity as arising from a latent clustering mechanism, where the number of clusters is unknown and estimated. Building on the mixture of finite mixtures (MFM) approach,

Yuzhi Yang, Sen Yan, Weijie Zhou, Brahim Mefgouda

With the development of artificial intelligence (AI) techniques, implementing AI-based techniques to improve wireless transceivers becomes an emerging research topic. Within this context, AI-based channel characterization and estimation become the focus since these methods have not been solved by traditional methods very well and have become the bottleneck o

Zhiyong Yu, Wanying Yue

This paper is concerned with a kind of linear-quadratic (LQ, for short) two-person zero-sum stochastic differential game problems with partial observation. We propose the notions of explicit and implicit feedback laws under partial observation. With the help of a class of conditional mean-field stochastic differential equations (CMF-SDEs, for short), the sep

Alexei A. Deriglazov

Any given system of ordinary differential equations in $n$-dimensional configuration space can be obtained from a peculiar variational problem with one local symmetry. The obtained action functional leads to the Hamiltonian formulation in $(4n+2)$-dimensional phase space. As concrete examples, we discuss the cases of Lagrange-d'Alembert equations with nonlin

Philipp Dorau, Albert Much

We provide arguments indicating that the semiclassical Einstein equations follow from quantum relative entropy and its proportionality to an area variation. Using modular theory, we establish that the relative entropy between the vacuum state and coherent excitations of a scalar quantum field on a bifurcate Killing horizon is given by the energy flux across

Joseph McDonough, Pavlo Pylyavskyy, Shiyun Wang

Let $U$ be a tensor product of highest weight modules of $GL_n(\mathbb C)$ corresponding to multiples of fundamental weights (i.e. rectangles). We consider three ways to stratify $U^{\otimes k}$ into components: using isotypic components of the cyclic action on tensor factors, using a generalization of the charge statistic, and using certain generalizations

Liqian Qin, Yuchao Tang, Jigen Peng

In this work, we propose a new splitting algorithm for solving structured monotone inclusion problems composed of a maximally monotone operator, a maximally monotone and Lipschitz continuous operator and a cocoercive operator. Our method augments the forward-backward-half forward splitting algorithm with a nonlinear momentum term. Under appropriate condition

Katherine Abramski, Giulio Rossetti, Massimo Stella

As Large language models (LLMs) become increasingly integrated into our lives, their inherent social biases remain a pressing concern. Detecting and evaluating these biases can be challenging because they are often implicit rather than explicit in nature, so developing evaluation methods that assess the implicit knowledge representations of LLMs is essential

Konstantinos Konstantos, Pavlos Motakis

For every $1\leq \alpha<\omega_1$, we construct an explicit unconditional finite-dimensional decomposition (FDD) $(X_\lambda)_{\lambda\in\mathcal{T}_\alpha}$ of the Bourgain-Rosenthal-Schechtman space $R_\alpha^{p,0}$ by blocking its standard martingale difference sequence (MDS) basis. This FDD has strong reproducing properties and supports a theory of distr

Tinsae G. Dulecha, Leonardo Righetto, Ruggero Pintus, Enrico Gobbetti

Reflectance Transformation Imaging (RTI) is very popular for its ability to visually analyze surfaces by enhancing surface details through interactive relighting, starting from only a few tens of photographs taken with a fixed camera and variable illumination. Traditional methods like Polynomial Texture Maps (PTM) and Hemispherical Harmonics (HSH) are compac

Daniel Meikle, Adri Olde Daalhuis

In solving $q$-difference equations, and in the definition of $q$-special functions, we encounter formal power series in which the $n$th coefficient is of size $q^{-\binom{n}{2}}$ with $q\in(0,1)$ fixed. To make sense of these formal series, a $q$-Borel-Laplace resummation is required. There are three candidates for the $q$-Laplace transform, resulting in th

Anindita Sarkar, Paranjoy Chaki, Priya Ghosh, Ujjwal Sen

We propose a general framework to compare the values of a physical quantity pertaining to two - or more - physical setups, in the finite-precision scenario. Such a situation requires us to compare between two "patches" on the real line instead of two numbers. Identification of extent of the patches is typically done via standard deviation, as obtained within

Michele Lanza

In June 2024 I co-organized the FUture of Software Engineering symposium in Okinawa, Japan. Me, Andrian Marcus, Takashi Kobayashi and Shinpei Hayashi were general chairs, Nicole Novielli, Kevin Moran, Yutaro Kashiwa and Masanari Kondo were program chairs, some members of my group, Carmen Armenti, Stefano Campanella, Roberto Minelli, were the tables, can't ha

Klemens Iten, Lenart Treven, Bhavya Sukhija, Florian Dörfler

Reinforcement learning algorithms are typically designed for discrete-time dynamics, even though the underlying real-world control systems are often continuous in time. In this paper, we study the problem of continuous-time reinforcement learning, where the unknown system dynamics are represented using nonlinear ordinary differential equations (ODEs). We lev

Guanglong Yu, Lin Sun

This paper investigates the maximum spectral radius of planar graphs with concrete fixed number of vertices, providing some tight bounds on the maximum spectral radius of general planar graph resorting to its order, and confirming that among all planar graphs containing dominating vertex with concrete fixed order $n \geq 48$, the join of $P_{2}$ and $P_{n-2}

Qing Xue, Yun Lan, Jiajia Guo, Qianbin Chen

Targeting the requirements of 6G, this paper investigates a semi-passive dual-reconfigurable intelligent surface (RIS)-assisted integrated sensing and communication (ISAC) system, tackling the max-min user signal-to-interference-plus-noise ratio (SINR) problem via joint active and passive beamforming to enhance system performance and ensure user fairness. Ad

Yuanhang Qian, Xueqin Luo, Jilu Jin, Gongping Huang

Linear superarrays (LSAs) have been proposed to address the limited steering capability of conventional linear differential microphone arrays (LDMAs) by integrating omnidirectional and directional microphones, enabling more flexible beamformer designs. However, existing approaches remain limited because array geometry and element directivity, both critical t

Sebastian Petersen

The weak Hilbert property (WHP) for varieties over fields of characteristic zero was introduced by Corvaja and Zannier in 2017. There exist integral variants of WHP for arithmetic schemes. We present new fibration theorems for both the WHP and its integral analogue. Our primary fibration result, in a sense dual to the mixed fibration theorems of Javanpeykar

Frederik Broy, Maike Züfle, Jan Niehues

Scientific talks are a growing medium for disseminating research, and automatically identifying relevant literature that grounds or enriches a talk would be highly valuable for researchers and students alike. We introduce Reference Prediction from Talks (RPT), a new task that maps long, and unstructured scientific presentations to relevant papers. To support

Johanna Hartke

Planetary nebulae (PNe) are valuable spatial and kinematic tracers of nearby galaxies. In this mini-review, I focus on their role in tracing the halo and intra-cluster/intra-group light assembly in groups and clusters of galaxies within 100~Mpc and, in particular, the link between characteristic PN metrics such as the $\alpha$-parameter and the PN luminosity

Yihan Li, Xiyuan Fu, Ghanshyam Verma, Paul Buitelaar

Hallucination remains one of the key obstacles to the reliable deployment of large language models (LLMs), particularly in real-world applications. Among various mitigation strategies, Retrieval-Augmented Generation (RAG) and reasoning enhancement have emerged as two of the most effective and widely adopted approaches, marking a shift from merely suppressing

Hao Wang, Licheng Pan, Yuan Lu, Zhichao Chen

The design of training objective is central to training time-series forecasting models. Existing training objectives such as mean squared error mostly treat each future step as an independent, equally weighted task, which we found leading to the following two issues: (1) overlook the label autocorrelation effect among future steps, leading to biased training

Ulrik S. Fjordholm, Magnus C. Ørke

Scalar conservation laws sit at the intersection between being simple enough to study analytically, while being complex enough to exhibit a wide range of nonlinear phenomena. We introduce a novel stochastic perturbation of scalar conservation laws, inspired by mean field games. We prove well-posedness of the stochastically perturbed equation; prove that it c

Kyungmin Lee, Sihyun Yu, Jinwoo Shin

Denoising generative models, such as diffusion and flow-based models, produce high-quality samples but require many denoising steps due to discretization error. Flow maps, which estimate the average velocity between timesteps, mitigate this error and enable faster sampling. However, their training typically demands architectural changes that limit compatibil

Lucas Buk Cardoso, Simone Aldrey Angelo, Yasmin Pacheco Gil Bonilha, Fernando Maia

This study presents a comparative methodological analysis of six machine learning models for survival analysis (MLSA). Using data from nearly 45,000 colorectal cancer patients in the Hospital-Based Cancer Registries of S\~ao Paulo, we evaluated Random Survival Forest (RSF), Gradient Boosting for Survival Analysis (GBSA), Survival SVM (SSVM), XGBoost-Cox (XGB

Alvan Arulandu, Daniel Gottschalk, Thomas Payne, Alexander Richardson

We introduce a new measure of distance between datasets, based on vineyards from topological data analysis, which we call the vineyard distance. Vineyard distance measures the extent of topological change along an interpolation from one dataset to another, either along a pre-computed trajectory or via a straight-line homotopy. We demonstrate through theoreti

Yujie Zhu, Jilu Jin, Xueqin Luo, Wenxing Yang

Dereverberation has long been a crucial research topic in speech processing, aiming to alleviate the adverse effects of reverberation in voice communication and speech interaction systems. Among existing approaches, forward convolutional prediction (FCP) has recently attracted attention. It typically employs a deep neural network to predict the direct-path s

Arunkumar C. S., Sruthymurali

Inspired by the classical Bohman-Korovkin-Wulbert (BKW) operators, we initiate a study of noncommutative BKW-operators. Let $A$ be a unital $C^*$-algebra, and $S$ be a set of generators of $A$. A unital completely positive (UCP)-map $\phi: A\rightarrow B(H)$ is said to be a \textit{noncommutative BKW-operator} for $S$ with respect to norm or weak operator to

Durga Prasad Maram, Dhruvin Gandhi, Zonghai Yao, Gayathri Akkinapalli

Personalized text generation requires models not only to produce coherent text but also to align with a target user's style, tone, and topical focus. Existing retrieval-augmented approaches such as LaMP and PGraphRAG enrich profiles with user and neighbor histories, but they stop at generation and often yield outputs that drift in tone, topic, or style. We p

Weike Deng, Sheng Long, Qin Tan, Jiliang Jing

We investigate geodesic motion and gravitational-wave signatures of charged black holes with scalar hair. Using the effective potential approach, we analyze marginally bound orbits and innermost stable circular orbits, showing how their positions and energy thresholds are modified by the scalar hair parameter $r_B$. These results demonstrate scalar hair's ro

Stanisław M. S. Halkiewicz

We formalize the paradox of an omniscient yet lazy investor - a perfectly informed agent who trades infrequently due to execution or computational frictions. Starting from a deterministic geometric construction, we derive a closed-form expected profit function linking trading frequency, execution cost, and path roughness. We prove existence and uniqueness of

Alexandru Crăciun, Debarghya Ghoshdastidar

The theory of training deep networks has become a central question of modern machine learning and has inspired many practical advancements. In particular, the gradient descent (GD) optimization algorithm has been extensively studied in recent years. A key assumption about GD has appeared in several recent works: the \emph{GD map is non-singular} -- it preser

Seung Gyo Jeong, Minjae Kim, Jin Young Oh, Youngeun Ham

Engineering van Hove singularities (vHss) near the Fermi level, if feasible, offers a powerful route to control exotic quantum phases in electronic and magnetic behaviors. However, conventional approaches, which rely primarily on chemical and electrical doping, focus mainly on local electrical or optical measurements, limiting their applicability to coupled

Charles Javerliat, Pierre Raimbaud, Guillaume Lavoué

Markerless multiview motion capture is often constrained by the need for precise camera calibration, limiting accessibility for non-experts and in-the-wild captures. Existing calibration-free approaches mitigate this requirement but suffer from high computational cost and reduced reconstruction accuracy. We present Kineo, a fully automatic, calibration-free

Hao Jia, Shanglin Yang, Jiajun He, Shuo Liu

Topolectrical circuits provide a versatile platform for exploring and simulating modern physical models. However, existing approaches suffer from incomplete programmability and ineffective feature prediction and control mechanisms, hindering the investigation of physical phenomena on an integrated platform and limiting their translation into practical applic

Omar Morandi

We study the optimal control problem applied to a gas of particles with spin confined in a material with Rashba spin-orbit coupling effect, in the presence of an external magnetic field. The evolution of the particle gas is described in the Wigner formalism. We investigate the classical limit of the optimal control problem, and we prove the convergence of th

Korneel Van den Berghe, Stein Stroobants, Vijay Janapa Reddi, G. C. H. E. de Croon

Neuromorphic computing systems are set to revolutionize energy-constrained robotics by achieving orders-of-magnitude efficiency gains, while enabling native temporal processing. Spiking Neural Networks (SNNs) represent a promising algorithmic approach for these systems, yet their application to complex control tasks faces two critical challenges: (1) the non

Jiarong Yao, Chaopeng Tan, Meng Wang, Wei Ma

Reliable estimation of network-wide traffic states is essential for urban traffic management. Unmanned Aerial Vehicles (UAVs), with their airborne full-sample continuous trajectory observation, bring new opportunities for traffic state estimation. In this study, we will explore the optimal UAV deployment problem in road networks in conjunction with ground se

Habtom Kahsay Gidey, Niklas Huber, Alexander Lenz, Alois Knoll

The autonomy of software agents is fundamentally dependent on their ability to construct an actionable internal world model from the structured data that defines their digital environment, such as the Document Object Model (DOM) of web pages and the semantic descriptions of web services. However, constructing this world model from raw structured data present

Samuel Talkington, Dmitrii M. Ostrovskii, Daniel K. Molzahn

We present an algorithm that efficiently computes nearly-optimal solutions to a class of combinatorial reconfiguration problems on weighted, undirected graphs. Inspired by societally relevant applications in networked infrastructure systems, these problems consist of simultaneously finding an unreweighted sparsified graph and nodal potentials that satisfy fi

Violet B, John M. Conroy, Sean Lynch, Danielle M

Analysts require attribution, as nothing can be reported without knowing the source of the information. In this paper, we will focus on automatic methods for attribution, linking each sentence in the summary to a portion of the source text, which may be in one or more documents. We explore using a hybrid summarization, i.e., an automatic paraphrase of an ext

Vivek Chetia, Abdul Taher Khan, Rahish Gogoi, David Kapsian Khual

The proposed solution is Deep Learning Technique that will be able classify three types of tea leaves diseases from which two diseases are caused by the pests and one due to pathogens (infectious organisms) and environmental conditions and also show the area damaged by a disease in leaves. Namely Red Rust, Helopeltis and Red spider mite respectively. In this